IPL Cricket Matches & Ball-by-Ball Data (2008-2024)

This dataset provides a detailed ball-by-ball record of all IPL matches from 2008 to 2024, covering match details, player performances, and game outcomes. It is ideal for sports analytics, match predictions, and player performance evaluation.

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📌 Dataset Overview

• Matches Data: Covers match date, venue, teams, toss results, winners, and match outcomes.
• Ball-by-Ball Data: Tracks every legal and extra delivery, including batsmen, bowlers, runs, and wickets.
• Dismissals & Wickets: Includes details on player dismissals, types (bowled, caught, run-out, etc.), and involved fielders.
• Innings & Overs: Records innings progression, legal deliveries, extras, and Super Overs (if applicable).

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📊 Column Descriptions

📂 Match & Inning Details

Column Name	Description
match_id	Unique ID for each match.
season	Year of the IPL season (2008-2024).
venue	Stadium where the match was played.
date	Date of the match.
match_type	Format of the match (T20).
inning	Inning number (1st or 2nd, more if Super Over).

🏏 Team & Toss Information

Column Name	Description
batting_team	Team currently batting.
bowling_team	Team currently bowling.
team1	One of the competing teams.
team2	Opposing team in the match.
toss_winner	Team that won the toss.
toss_decision	Decision made by toss-winning team (bat or field).
winner	Team that won the match.

🔄 Over & Ball Tracking

Column Name	Description
over	Over number (0-19 in T20).
ball	Ball number within an over (1-6, can exceed due to extras).
is_legal	Boolean (1 for legal, 0 for extras like wides & no-balls).
legal_ball	Count of legal deliveries within an over.
all_balls_over_ball	Over and ball combined as a decimal (includes all balls).
adjusted_over_ball	Over and ball combined as a decimal (only legal deliveries).

👤 Player Details

Column Name	Description
batter	Batsman on strike.
bowler	Bowler delivering the ball.
non_striker	Batsman at the non-striker’s end.

🏆 Runs & Extras

Column Name	Description
batsman_runs	Runs scored by the batsman on that ball.
extra_runs	Additional runs (wides, no-balls, leg byes, etc.).
total_runs	Sum of batsman’s runs and extras in that ball.
extras_type	Type of extra (wide, leg bye, no-ball, etc.).

❌ Wicket Details

Column Name	Description
is_wicket	Boolean (1 for wicket, 0 otherwise).
player_dismissed	Name of the dismissed player (if any).
dismissal_kind	Type of dismissal (bowled, caught, LBW, etc.).
fielder	Fielder involved in the dismissal (if applicable).

📊 Match-Level Aggregations

Column Name	Description
match_number_of_that_season	Match number within that season.
matches_in_that_season	Total matches played in that season.
match_number_in_total	Overall match number in the dataset.

📌 Other Columns

Column Name	Description
id	Duplicate of match_id (retained from merge).
player_of_match	Player awarded as "Man of the Match".
result	Match outcome (win, tie, no result).
result_margin	Margin of victory (runs or wickets).
target_runs	Target score set for the chasing team.
target_overs	Number of overs available to chase the target.
super_over	Boolean (1 if a Super Over was played).

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⚠️ Cause of Concerns

While this dataset provides comprehensive IPL match and ball-by-ball data, a few limitations should be considered:

1. Data Accuracy & Completeness – The dataset is compiled from various sources; minor discrepancies in player names, venues, or match details may exist.
2. Missing Data for Specific Matches – Some matches, especially rain-affected ones, might have incomplete or missing ball-by-ball details.
3. Super Over Handling – Matches with Super Overs have additional innings, which might require special handling in analysis.
4. Merging Duplicates – Some columns (id and match_id) exist due to merging; redundant columns should be handled carefully in analysis.
5. Evolving IPL Formats – Rule changes over the years (e.g., impact player rule, changes in points system) may impact trend analysis.
6. Toss & Match Outcome Bias – The dataset doesn’t directly account for external factors like pitch conditions, weather, or DLS calculations.
7. Player Name Variations – Some players might have inconsistent name formats (e.g., "MS Dhoni" vs. "M.S. Dhoni"), requiring preprocessing for accurate analysis.

🔍 How to Mitigate?

• Data Cleaning: Handle missing values, standardize player names, and drop redundant columns.
• Feature Engineering: Consider match conditions (home/away advantage, team strengths) for better predictive models.
• Super Over Handling: Filter separately for Super Overs before aggregating statistics.

⚠️ Keep these factors in mind when conducting analysis to ensure accurate insights!

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🎯 Potential Use Cases

✔️ Win Prediction: Predict match outcomes based on historical trends.
✔️ Player Performance Analysis: Track runs, wickets, and match-winning contributions.
✔️ Team Strategies & Toss Impact: Analyze toss-winning decisions and their effect on results.
✔️ Exploratory Data Analysis (EDA): Build visualizations and dashboards for IPL insights.

📢 If you find this dataset useful, don’t forget to upvote! 🚀